Job Performance Optimization Method Based on Data Balance in the Wireless Sensor Networks

Authors

  • Ziyu Sun School of Computer Science and Engineering, Luoyang Institute of Science and Technology
  • Guozeng Zhao School of Computer Science and Engineering, Luoyang Institute of Science and Technology
  • Meng Li School of Computer Science and Engineering, Luoyang Institute of Science and Technology
  • Zhiguo Lv State Key Laboratory of Integrated Services Networks, Xidian University

DOI:

https://doi.org/10.3991/ijoe.v13i12.7882

Keywords:

wireless sensor networks, MapReduce, hash function, data skew, heterogeneity-aware

Abstract


In the wireless sensor network, the representative MapReduce computing model based on data center has been widely used in large-scale data processing. In the data transmission phase, the wireless sensor network system uses the hash method to distribute data for each Reduce task based on the number of Reduce tasks. This data partitioning method based on the hash function results in non-uniform distribution of the output data in the data transmission phase and further leads to skewing of the input data in the Reduce task. Data skew will result in load imbalance in the Reduce phase and causes the system performance to degrade. In order to eliminate the data skew problem in the Reduce phase, this paper presents a load balancing method, which consists of two parts: the virtual partitioning method based on the consistent hashing and the heterogeneity-aware loads balancing (HLB) algorithm. The experimental results show that the proposed method can eliminate the data skew in the Reduce phase and distribute the load equitably for each Reduce task. In addition, the method produces less system overhead.

Author Biographies

Ziyu Sun, School of Computer Science and Engineering, Luoyang Institute of Science and Technology

Sun Zeyu is an associate professor in School of Computer and Information Engineering, Luoyang Institute of Science and Technology, Luoyang, China. His research interests include wireless sensor networks, internet of things, cloud computing.

Guozeng Zhao, School of Computer Science and Engineering, Luoyang Institute of Science and Technology

Zhao Guozeng (Corresponding author) is a lecture in School of Computer and Information Engineering, Luoyang Institute of Science and Technology, Luoyang, China. His research interests include wireless sensor networks, cloud computing.

Meng Li, School of Computer Science and Engineering, Luoyang Institute of Science and Technology

Li Meng is a professor in School of Computer and Information Engineering, Luoyang Institute of Science and Technology, Luoyang, China. His research interests include wireless sensor networks, internet of things, cloud computing.

Zhiguo Lv, State Key Laboratory of Integrated Services Networks, Xidian University

Lv Zhiguo received the B.S. degree in applied electronic technology from Henan Normal University, Xinxiang, China, in 2000, the M.S. degree in communications engineering from Guilin University of Electronic Technology, Guilin, China, in 2008. Since 2008, he has been on the faculty of Luoyang Institute of Science and Technology. He is currently working toward the Ph.D. degree at Xidian University, His research interests include networks communication, internet of things.

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Published

2017-12-11

How to Cite

Sun, Z., Zhao, G., Li, M., & Lv, Z. (2017). Job Performance Optimization Method Based on Data Balance in the Wireless Sensor Networks. International Journal of Online and Biomedical Engineering (iJOE), 13(12), pp. 4–17. https://doi.org/10.3991/ijoe.v13i12.7882

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Section

Papers